Regularized single-kernel conditional density estimation for face description

Robinson, John A

doi:10.1109/icip.2009.5414095

Cited by 3 publications

(7 citation statements)

References 9 publications

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“…Conditional density estimation (CDE) [10], [11] is method that estimates unknown features according to known features of an observation by a pretrained estimation model, which is trained by the same type of data. Assume that there is an observation in a multidimensional distribution, and the features of the observation are partly known and the rest are unknown.…”

Section: Conditional Density Estimationmentioning

confidence: 99%

“…To further investigate the relationship between REE and the arcing phenomenon, a method combining multiple characteristics for REE prediction is expected. Based on conditional density estimation (CDE) [10], [11], this paper proposes a novel prediction method that applies several arcing characteristics computed from electrical signals. The method is designed to not only recognize the failure condition of an ac contactor but also estimate the endurance condition throughout the entire lifetime.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Residual Electrical Endurance Prediction Method for Low-Voltage Electromagnetic Alternating Current Contactors

Huang

et al. 2015

IEEE Trans. Compon., Packag. Manufact. Technol.

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This paper proposes a novel residual electrical endurance (REE) prediction modeling method for low-voltage electromagnetic alternating current (ac) contactors based on conditional density estimation (CDE). For a type of ac contactor, the current REE value is defined by the ratio of the current operation number against its maximum operation number. Voltage and current signals between contacts during breaking processes are acquired as the initial data. An arcing characteristic set of every breaking process is computed from the voltage/current signals, and the characteristic sets are grouped and averaged to achieve training and testing data sets. Four data schemes are applied and compared. CDE modeling with cross-validation is performed on the training data set to build REE prediction models, which are then applied to estimate the REEs of testing observations. The experiment proves that the modeling method is able to reach relatively precise predictions. Index Terms-Alternating current (ac) contactors, conditional density estimation (CDE), residual electrical endurance (REE).Yingmin You received the M.Sc. degree from Chongqing University, Chongqing, China, in 2009.He is currently an Experimentalist with the Key . His current research interests include intelligence and reliability of electrical apparatuses, and theories and experiments on electrical discharges and plasmas.

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Section: Conditional Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Residual Electrical Endurance Prediction Method for Low-Voltage Electromagnetic Alternating Current Contactors

Huang

et al. 2015

IEEE Trans. Compon., Packag. Manufact. Technol.

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“…Four classification methods were used: Least-square SVM [11], Single-Kernel CDE [6], threshold Adaboost and, finally, Convolutional NN. For Adaboost we tried with different input features; either normalized pixel values, or Haarfeatures [12], or Gabor-filtered images.…”

Section: Technical Backgroundmentioning

confidence: 99%

“…The single-kernel conditional density estimation system proposed in [6] obtains 39 descriptive parameters including gender, age, ethnicity, pose and expression from images of faces. The author used 24x24 images, and concatenated to its vector the other attributes forming a single vector used for training;…”

Section: A Single-kernel Cdementioning

confidence: 99%

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A Mixture of Two Gender Classification Experts

El-Din

Moustafa

Mahdi

2012

2012 25th SIBGRAPI Conference on Graphics, Patterns and Images

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This paper presents a novel method for combining the outputs of different gender classification techniques based on facial images. Merging the methods is performed by a committee machine using the Bayesian theorem.We implement and compare several well-known individual classifiers on four different datasets, then we experiment the proposed machine, and show that it significantly improves the accuracy of classification compared to individual classifiers. We also include results that address the effect of scale on the performance of classifiers. I. INTRODUCTIONFacial analysis has been widely investigated in computer vision, including gender, age and expression classification. In particular, gender discrimination is important for several applications; it can improve the performance systems of face verification [1] and face recognition by using separate models for each gender [2], [3], it can help index and retrieve images [4], and it is useful for training interaction systems that behave differently according to the gender of the user.The accuracy of individual gender classification methods can be boosted by merging more than one classifier [5]. When these classifiers use different input features extracted from the face; there is a higher probability that their false classifications on a set of images are disjoint, in which case merge is helpful to minimize the final error of the combined classifier.In this paper we propose a committee machine for merging classification methods based on naive Bayesian theorem, and show, on four different image databases, how this combination improves the performance over the best single constituent classifier by up to more than 4%.The paper is organized as follows; Section II presents an overview of the previous related work. Section III describes the individual classification methods used and Section IV introduces our proposed method for merging these classifiers. Section V explains the experiments we carried and the databases we used, then the results achieved. In the final section, we conclude our work.

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Software‐Based Evaluation of Human Age: A Pilot Study

Patzelt

Schaible

Stampf

et al. 2014

J Esthet Restor Dent

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This study gives a better understanding about the reliability of a software-based evaluation tool for age and identifies factors (e.g., the visibility of the teeth) potentially affecting the estimation of age. Naturally looking teeth seem to have no influence on the evaluation of a person's age. Thus, the application of this specific application for dental purposes is questionable; however, in forensics, it might be a valuable tool for estimating a person's age.

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Regularized single-kernel conditional density estimation for face description

Cited by 3 publications

References 9 publications

A Novel Residual Electrical Endurance Prediction Method for Low-Voltage Electromagnetic Alternating Current Contactors

A Novel Residual Electrical Endurance Prediction Method for Low-Voltage Electromagnetic Alternating Current Contactors

A Mixture of Two Gender Classification Experts

Software‐Based Evaluation of Human Age: A Pilot Study

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